from scipy.stats import wilcoxon, chisquare
# import matplotlib.pyplot as plt
# from hcluster import *
import Assurance as ass
import Parser as parser
import Experiment1 as exp1
import Experiment5 as exp5
import Experiment6 as exp6
import CrossValidation as cross
import SVMValidatedExperiment as exp7
def initialize():
trials = parser.parse()
atributos = ["type", "orientation", "age", "hairColour", "hasBeard", "hasHair", "hasGlasses", "hasShirt", "hasTie", "hasSuit", "x-dimension", "y-dimension"]
return trials, atributos
if __name__ == '__main__':
trials, atributos = initialize()
# trials = exp1.run(trials, atributos)
folds = cross.crossValidation(10, trials)
# exp5.run(folds, atributos, 0.7)
# exp6.run(folds, atributos, 0.7)
# exp7.run(trials, folds, atributos, {}, False)
exp7.run(trials, folds, atributos, {}, True)
if __name__ == '__main__':
dominios, anotacoes, atributos, targets = initialize()
#anotacoes = exp1.run(dominios, anotacoes, atributos, targets)
folds = cross.crossValidation(10, anotacoes)
#folds = exp5.run(dominios, folds, atributos, targets, 0.7)
# folds = exp6.run(dominios, folds, atributos, targets, 0.7)
print "Machine Learning sem ID"
# exp7.run(dominios, targets, folds, atributos, {}, False)
print "Machine Learning com ID"
exp7.run(dominios, targets, folds, atributos, {}, True)
# dice = []
# diceGlobal = []
# dicePersonalizado = []
#
# diceGlobalSuperespecificado = []
# dicePersonalizadoSuperespecificado = []
# print "DICE \t DICE GLOBAL \t DICE PERSONALIZADO \t"
# for fold in folds:
# for participante in folds[fold].keys():
# dice.append(folds[fold][participante]["dice"])
# diceGlobal.append(folds[fold][participante]["dice_global"])
# dicePersonalizado.append(folds[fold][participante]["dice_personalizado"])
#
# diceGlobalSuperespecificado.append(folds[fold][participante]["dice_global_superespecificado"])
# from hcluster import *
import ParserStars as parser
import CrossValidation as cross
import Experiment1 as exp1
import SVMValidatedExperiment as exp2
import SVMValidatedExperiment2 as exp3
import ExperimentDecisionTree as exp4
import ValidatedExperimentIndividual as exp5
def initialize():
anotacoes = parser.parseAnnotation()
dominios = parser.parseDominio()
participantes = {}
atributos = ["type", "size", "colour", "hpos", "vpos", "near", "left", "right", "below", "above", "in-front-of"]
targets = {"01f-t1n":"h", "01f-t1r":"h", "01f-t2n":"h", "01f-t2r":"h", "01o-t1n":"h", "01o-t1r":"h", "01o-t2n":"h", "01o-t2r":"h", "02f-t1n":"o", "02f-t1r":"o", "02f-t2n":"o", "02f-t2r":"o", "02o-t1n":"o", "02o-t1r":"o", "02o-t2n":"o", "02o-t2r":"o", "03f-t1n":"m", "03f-t1r":"m", "03f-t2n":"m", "03f-t2r":"m", "03o-t1n":"m", "03o-t1r":"m", "03o-t2n":"m", "03o-t2r":"m", "04f-t1n":"a", "04f-t1r":"a", "04f-t2n":"a", "04f-t2r":"a", "04o-t1n":"a", "04o-t1r":"a", "04o-t2n":"a", "04o-t2r":"a", "05f-t1n":"m", "05f-t2n":"m", "05f-t1r":"m", "05f-t2r":"m", "05o-t1n":"m", "05o-t1r":"m", "05o-t2n":"m", "05o-t2r":"m", "06f-t1n":"h", "06f-t1r":"h", "06f-t2n":"h", "06f-t2r":"h", "06o-t1n":"h", "06o-t1r":"h", "06o-t2n":"h", "06o-t2r":"h", "07f-t1n":"i", "07f-t1r":"i", "07f-t2n":"i", "07f-t2r":"i", "07o-t1n":"i", "07o-t1r":"i", "07o-t2n":"i", "07o-t2r":"i", "08f-t1n":"a", "08f-t1r":"a", "08f-t2n":"a", "08f-t2r":"a", "08o-t1n":"a", "08o-t1r":"a", "08o-t2n":"a", "08o-t2r":"a" }
return dominios, targets, anotacoes, atributos, participantes
if __name__ == '__main__':
dominios, targets, anotacoes, atributos, participantes = initialize()
folds = cross.crossValidation(10, anotacoes)
print "Machine Learning sem ID"
# exp5.run(dominios, targets, anotacoes, atributos, False)
exp2.run(dominios, targets, folds, atributos, {}, False)
print "\n\n"
print "Machine Learning com ID"
# exp5.run(dominios, targets, anotacoes, atributos, True)
exp2.run(dominios, targets, folds, atributos, {}, True)
# import matplotlib.pyplot as plt
# from hcluster import *
import Assurance as ass
import ParserGRE3D as parser
import CrossValidation as cross
import Experiment1 as exp1
import SVMValidatedExperiment as exp2
import ExperimentDecisionTree as exp4
import ValidatedExperimentIndividual as exp5
def initialize():
anotacoes = parser.parseAnnotation()
dominios = parser.parseDominio()
participantes = parser.parseParticipantes()
targets = {"1":"b3","2":"b2","3":"b3","4":"b2","5":"b3","6":"b1","7":"b3","8":"b1","9":"b2","10":"b1","11":"b2","12":"b1","13":"b3","14":"b1","15":"b3","16":"b1","17":"b1","18":"b1","19":"b1","20":"b1","21":"b1","22":"b1","23":"b1","24":"b1","25":"b4","26":"b3","27":"b4","28":"b3","29":"b3","30":"b3","31":"b3","32":"b3"}
atributos = ['loc', 'left-of', 'next-to', 'on-top-of', 'right-of', 'type', 'col', 'size']
return dominios, targets, anotacoes, participantes, atributos
if __name__ == '__main__':
dominios, targets, anotacoes, participantes, atributos = initialize()
folds = cross.crossValidation(10, anotacoes)
print "Machine Learning sem ID"
# exp5.run(dominios, targets, anotacoes, atributos, participantes, False)
exp2.run(dominios, targets, folds, atributos, participantes, False)
print "\n\n"
print "Machine Learning com ID"
# exp5.run(dominios, targets, anotacoes, atributos, participantes, True)
# exp2.run(dominios, targets, folds, atributos, participantes, True)
